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  • Odds ratio of my interaction variable is too big

    Hello,

    I am writing my research on the determinants of bribing. I am using interaction variables in my logistic regression. My data is from a survey with oversampling in six regions. After reading in this forum that I could use [control = pweight] in my command, I decided not to use svy: set, since I need to report my pseudo R2.
    However, now I am not quite sure about the result, because I think the odds ratio of the interaction variable kis##health is too big. I am quite new with stata and statistic. Thus I need your advice on this matter.
    Both my interaction variables are dummy: KIS = poor people = 1; health : poor perception on quality of health service = 1

    my output is

    Code:
     
    logit brihealth kis##health urban age gender education employment business religius1 value $controls[pw = BOT_NAS_JBR_JTG], or robus
    > t nolog
    Logistic regression Number of obs = 1,373
    Wald chi2(11) = 81.68
    Prob > chi2 = 0.0000
    Log pseudolikelihood = -210.62002 Pseudo R2 = 0.1182
    Robust
    brihealth Odds Ratio Std. Err. z P>z [95% Conf. Interval]
    1.kis .6619124 .179435 -1.52 0.128 .3890916 1.126028
    1.health .6359187 .3877471 -0.74 0.458 .1924807 2.100951
    kis#health
    1 1 11.5063 8.774819 3.20 0.001 2.581076 51.29446
    urban .6217023 .1611696 -1.83 0.067 .3740397 1.033349
    age .9574096 .0090956 -4.58 0.000 .9397476 .9754036
    gender .3653943 .1218295 -3.02 0.003 .190088 .7023746
    education .8099218 .1092265 -1.56 0.118 .6217984 1.054961
    employment .6651625 .2377409 -1.14 0.254 .3301363 1.340177
    business 2.459535 .8240591 2.69 0.007 1.275442 4.742914
    religius1 1.008044 .1638453 0.05 0.961 .7330385 1.386219
    value .4081261 .0997453 -3.67 0.000 .2527913 .6589107
    _cons 2.904753 2.118633 1.46 0.144 .695457 12.13244
    Is there anything wrong with the data? Before I use the controls pweight the odds ratio is 7.

    If there is nothing wrong: Is it right if I interpret it as: the odds ratio of poor people with poor perception on health service ten times more likely to bribe than not poor people with good perception on the health service. I find this sentence is wrong, but I don't know how to fix it.

    Here is the dataex of my research.

    Code:
     
    Code:
    * Example generated by -dataex-. To install: ssc install dataex
    clear
    input float(brihealth kis health urban age gender education employment business religius1 value) double BOT_NAS_JBR_JTG
    0 1 0 0 54 0 0 1 0 4 1 .07809219214599998
    . 0 0 0 22 1 2 1 0 4 1 .07809219214599998
    . 1 0 0 64 0 1 1 0 4 0 .07809219214599998
    . . 0 0 52 1 0 1 0 4 1 .07809219214599998
    . 0 0 0 34 0 0 1 0 4 1 .07809219214599998
    0 0 0 0 32 1 0 1 0 4 1 .07809219214599998
    0 0 0 0 36 0 0 1 0 4 1 .07809219214599998
    . 1 0 0 59 1 0 1 0 4 1 .07809219214599998
    0 . 0 0 58 0 0 1 0 4 1 .07809219214599998
    . . 0 0 28 1 2 1 0 4 1 .07809219214599998
    0 0 0 0 19 0 2 0 0 . 1 .08627298325863374
    . . . 0 57 1 0 0 0 . . .08627298325863374
    0 . 0 0 50 0 0 1 0 4 1 .08627298325863374
    0 . 0 0 45 1 0 0 0 3 1 .08627298325863374
    0 0 0 0 37 0 1 1 0 4 1 .08627298325863374
    . . 0 0 35 1 0 0 0 . . .08627298325863374
    0 0 0 0 57 0 0 1 0 3 1 .08627298325863374
    . . 0 0 50 1 0 0 0 . . .08627298325863374
    0 0 0 0 53 0 0 1 0 3 1 .08627298325863374
    . . 0 0 38 1 0 0 0 4 1 .08627298325863374
    0 0 0 0 25 0 0 1 1 4 1 .09816057671983237
    . . 0 0 24 1 2 0 0 4 1 .09816057671983237
    . 0 0 0 67 0 0 0 0 4 1 .09816057671983237
    0 . 0 0 42 1 0 1 0 3 1 .09816057671983237
    0 0 0 0 45 0 0 1 0 4 1 .09816057671983237
    1 0 0 0 30 1 0 0 0 4 1 .09816057671983237
    . 1 0 0 55 0 2 1 0 4 1 .09816057671983237
    0 0 0 0 27 1 2 0 0 4 1 .09816057671983237
    0 1 0 0 44 0 1 1 0 4 1 .09816057671983237
    0 0 0 0 46 1 0 1 0 4 1 .09816057671983237
    0 0 0 0 36 0 1 1 0 3 1 .09816057671983237
    0 0 0 0 28 1 2 0 0 4 1 .09816057671983237
    0 0 0 0 29 0 1 1 0 3 1 .09816057671983237
    0 0 0 0 27 1 2 0 0 3 1 .09816057671983237
    0 . 0 0 59 0 2 1 0 4 1 .09816057671983237
    0 0 1 0 23 1 2 0 0 4 1 .09816057671983237
    . 0 0 0 58 0 2 1 1 4 1 .09816057671983237
    0 1 0 0 42 1 1 1 1 3 1 .07417418612448407
    . 0 0 0 60 0 0 1 0 4 1 .09816057671983237
    0 1 1 0 39 1 0 0 0 3 1 .09816057671983237
    . 1 0 0 48 0 1 1 0 4 1 .0868175767433184
    . 0 0 0 70 1 0 1 0 4 1 .0868175767433184
    . 1 0 0 47 0 0 1 0 4 1 .0868175767433184
    . 1 0 0 30 1 0 1 0 4 1 .0868175767433184
    . 1 0 0 35 0 1 1 0 4 1 .0868175767433184
    . 1 0 0 35 1 2 0 0 4 1 .0868175767433184
    . 0 0 0 45 0 2 1 0 4 1 .0868175767433184
    0 1 0 0 40 1 0 1 0 4 1 .0868175767433184
    0 0 0 0 48 0 2 1 0 3 1 .0868175767433184
    0 0 0 0 36 1 1 0 0 3 0 .07529966521257946
    0 1 0 0 31 0 2 1 0 4 1 .0868175767433184
    . 0 0 0 52 1 0 1 0 4 0 .0868175767433184
    . 0 0 0 23 0 2 1 0 3 1 .0868175767433184
    . 1 0 0 22 1 1 0 0 3 1 .0868175767433184
    . 0 0 0 60 0 0 1 0 4 1 .0868175767433184
    . 0 0 0 31 1 0 1 0 3 1 .0868175767433184
    . 0 0 0 34 0 1 1 0 4 1 .0868175767433184
    . 0 0 0 39 1 0 1 0 4 1 .0868175767433184
    0 0 0 0 67 0 0 0 0 4 1 .0868175767433184
    0 0 0 0 41 1 3 1 0 4 1 .0868175767433184
    0 0 0 0 40 0 1 1 0 4 1 .11662930745082345
    0 . 1 0 41 1 0 0 0 4 1 .11662930745082345
    0 . 1 0 50 0 0 1 0 4 1 .11662930745082345
    0 1 0 0 35 1 0 0 0 3 1 .11662930745082345
    0 0 0 0 19 0 1 1 0 4 1 .11662930745082345
    0 0 0 0 46 1 1 1 0 4 1 .11662930745082345
    0 0 0 0 21 0 2 1 0 4 1 .11662930745082345
    0 . 1 0 26 1 1 1 0 4 1 .11662930745082345
    0 1 0 0 50 0 0 1 0 4 1 .11662930745082345
    0 1 1 0 25 1 3 0 0 3 1 .11662930745082345
    1 . 0 0 35 0 2 0 0 4 1 .11662930745082345
    0 0 0 0 40 1 2 0 0 4 1 .10115633417167323
    . 0 0 0 48 0 0 1 0 4 1 .11662930745082345
    . . 0 0 47 1 1 0 0 4 1 .11662930745082345
    . 1 0 0 46 0 0 1 0 4 1 .11662930745082345
    . . 0 0 19 1 3 0 0 4 1 .11662930745082345
    . 0 0 0 46 0 0 1 0 4 1 .11662930745082345
    . 0 0 0 42 1 2 1 0 4 1 .11662930745082345
    . 0 0 0 29 0 3 1 0 4 1 .11662930745082345
    0 1 0 0 23 1 3 0 0 4 1 .11662930745082345
    . 0 0 1 52 0 2 1 0 4 1 .098587619521058
    . 0 0 1 44 1 1 0 0 4 1 .098587619521058
    . 0 0 1 37 0 1 1 0 4 1 .098587619521058
    0 1 0 1 35 1 0 0 0 4 1 .098587619521058
    . . 0 1 49 0 0 1 0 4 1 .098587619521058
    . 0 0 1 32 1 2 0 0 4 1 .098587619521058
    . 1 0 1 49 0 0 1 0 4 1 .098587619521058
    . . 0 1 19 1 1 0 0 4 1 .098587619521058
    0 1 0 1 19 0 2 0 0 4 1 .098587619521058
    . 1 0 1 50 1 0 0 0 4 1 .098587619521058
    . 1 0 1 46 0 2 1 0 4 0 .07209680654501288
    . 1 0 1 41 1 2 1 0 3 0 .07209680654501288
    . 0 0 1 46 0 2 1 0 4 1 .07209680654501288
    . 1 0 1 39 1 1 0 0 4 1 .07209680654501288
    . . 1 1 60 0 3 1 0 3 1 .07209680654501288
    . 0 0 1 34 1 0 1 1 3 1 .07209680654501288
    . 1 0 1 57 0 2 1 1 3 1 .05447932486087617
    . 0 0 1 51 1 3 1 0 3 1 .07209680654501288
    . 0 0 1 42 0 1 1 0 3 1 .07209680654501288
    . 0 0 1 46 1 3 1 0 4 1 .06253186969023976
    end
    Really appreciate your help on this matter

  • #2
    Hello,

    I am writing my research on the determinants of bribing. I am using interaction variables in my logistic regression. My data is from a survey with oversampling in six regions. After reading in this forum that I could use [control = pweight] in my command, I decided not to use svy: set, since I need to report my pseudo R2.
    However, now I am not quite sure about the result, because I think the odds ratio of the interaction variable kis##health is too big. I am quite new with stata and statistic. Thus I need your advice on this matter.
    Both my interaction variables are dummy: KIS = poor people = 1; health : poor perception on quality of health service = 1

    my output is

    Code:
    logit brihealth kis##health urban age    gender    education employment business religius1    value    $controls[pw    =    BOT_NAS_JBR_JTG],    or    robus
    > t nolog
    
    Logistic regression        Number of obs     =      1,373
            Wald chi2(11)     =      81.68
            Prob > chi2       =     0.0000
    Log pseudolikelihood = -210.62002        Pseudo R2         =     0.1182
    
            
    Robust
    brihealth  Odds Ratio   Std. Err.    z    P>z     [95% Conf. Interval]
            
    1.kis    .6619124    .179435    -1.52    0.128     .3890916    1.126028
    1.health    .6359187   .3877471    -0.74    0.458     .1924807    2.100951
                 
    kis#health 
    1 1      11.5063   8.774819    3.20    0.001     2.581076    51.29446
                 
    urban    .6217023   .1611696    -1.83    0.067     .3740397    1.033349
    age    .9574096   .0090956    -4.58    0.000     .9397476    .9754036
    gender    .3653943   .1218295    -3.02    0.003      .190088    .7023746
    education    .8099218   .1092265    -1.56    0.118     .6217984    1.054961
    employment    .6651625   .2377409    -1.14    0.254     .3301363    1.340177
    business    2.459535   .8240591    2.69    0.007     1.275442    4.742914
    religius1    1.008044   .1638453    0.05    0.961     .7330385    1.386219
    value    .4081261   .0997453    -3.67    0.000     .2527913    .6589107
    _cons    2.904753   2.118633    1.46    0.144      .695457    12.13244
    Is there anything wrong with the output? Before I use the controls pweight the odds ratio is 7.

    If there is nothing wrong: Is it right if I interpret it as: the odds ratio of poor people with poor perception on health service ten times more likely to bribe than not poor people with good perception on the health service. I find this sentence is wrong, but I don't know how to fix it.

    Here is the dataex of my research.

    Code:
    Code:
    * Example generated by -dataex-. To install: ssc install dataex
    clear
    input float(brihealth kis health urban age gender education employment    business    religius1    value)    double    BOT_NAS_JBR_JTG
    0 1 0 0 54 0 0 1 0 4 1 .07809219214599998
    . 0 0 0 22 1 2 1 0 4 1 .07809219214599998
    . 1 0 0 64 0 1 1 0 4 0 .07809219214599998
    . . 0 0 52 1 0 1 0 4 1 .07809219214599998
    . 0 0 0 34 0 0 1 0 4 1 .07809219214599998
    0 0 0 0 32 1 0 1 0 4 1 .07809219214599998
    0 0 0 0 36 0 0 1 0 4 1 .07809219214599998
    . 1 0 0 59 1 0 1 0 4 1 .07809219214599998
    0 . 0 0 58 0 0 1 0 4 1 .07809219214599998
    . . 0 0 28 1 2 1 0 4 1 .07809219214599998
    0 0 0 0 19 0 2 0 0 . 1 .08627298325863374
    . . . 0 57 1 0 0 0 . . .08627298325863374
    0 . 0 0 50 0 0 1 0 4 1 .08627298325863374
    0 . 0 0 45 1 0 0 0 3 1 .08627298325863374
    0 0 0 0 37 0 1 1 0 4 1 .08627298325863374
    . . 0 0 35 1 0 0 0 . . .08627298325863374
    0 0 0 0 57 0 0 1 0 3 1 .08627298325863374
    . . 0 0 50 1 0 0 0 . . .08627298325863374
    0 0 0 0 53 0 0 1 0 3 1 .08627298325863374
    . . 0 0 38 1 0 0 0 4 1 .08627298325863374
    0 0 0 0 25 0 0 1 1 4 1 .09816057671983237
    . . 0 0 24 1 2 0 0 4 1 .09816057671983237
    . 0 0 0 67 0 0 0 0 4 1 .09816057671983237
    0 . 0 0 42 1 0 1 0 3 1 .09816057671983237
    0 0 0 0 45 0 0 1 0 4 1 .09816057671983237
    1 0 0 0 30 1 0 0 0 4 1 .09816057671983237
    . 1 0 0 55 0 2 1 0 4 1 .09816057671983237
    0 0 0 0 27 1 2 0 0 4 1 .09816057671983237
    0 1 0 0 44 0 1 1 0 4 1 .09816057671983237
    0 0 0 0 46 1 0 1 0 4 1 .09816057671983237
    0 0 0 0 36 0 1 1 0 3 1 .09816057671983237
    0 0 0 0 28 1 2 0 0 4 1 .09816057671983237
    0 0 0 0 29 0 1 1 0 3 1 .09816057671983237
    0 0 0 0 27 1 2 0 0 3 1 .09816057671983237
    0 . 0 0 59 0 2 1 0 4 1 .09816057671983237
    0 0 1 0 23 1 2 0 0 4 1 .09816057671983237
    . 0 0 0 58 0 2 1 1 4 1 .09816057671983237
    0 1 0 0 42 1 1 1 1 3 1 .07417418612448407
    . 0 0 0 60 0 0 1 0 4 1 .09816057671983237
    0 1 1 0 39 1 0 0 0 3 1 .09816057671983237
    . 1 0 0 48 0 1 1 0 4 1  .0868175767433184
    . 0 0 0 70 1 0 1 0 4 1  .0868175767433184
    . 1 0 0 47 0 0 1 0 4 1  .0868175767433184
    . 1 0 0 30 1 0 1 0 4 1  .0868175767433184
    . 1 0 0 35 0 1 1 0 4 1  .0868175767433184
    . 1 0 0 35 1 2 0 0 4 1  .0868175767433184
    . 0 0 0 45 0 2 1 0 4 1  .0868175767433184
    0 1 0 0 40 1 0 1 0 4 1  .0868175767433184
    0 0 0 0 48 0 2 1 0 3 1  .0868175767433184
    0 0 0 0 36 1 1 0 0 3 0 .07529966521257946
    0 1 0 0 31 0 2 1 0 4 1  .0868175767433184
    . 0 0 0 52 1 0 1 0 4 0  .0868175767433184
    . 0 0 0 23 0 2 1 0 3 1  .0868175767433184
    . 1 0 0 22 1 1 0 0 3 1  .0868175767433184
    . 0 0 0 60 0 0 1 0 4 1  .0868175767433184
    . 0 0 0 31 1 0 1 0 3 1  .0868175767433184
    . 0 0 0 34 0 1 1 0 4 1  .0868175767433184
    . 0 0 0 39 1 0 1 0 4 1  .0868175767433184
    0 0 0 0 67 0 0 0 0 4 1  .0868175767433184
    0 0 0 0 41 1 3 1 0 4 1  .0868175767433184
    0 0 0 0 40 0 1 1 0 4 1 .11662930745082345
    0 . 1 0 41 1 0 0 0 4 1 .11662930745082345
    0 . 1 0 50 0 0 1 0 4 1 .11662930745082345
    0 1 0 0 35 1 0 0 0 3 1 .11662930745082345
    0 0 0 0 19 0 1 1 0 4 1 .11662930745082345
    0 0 0 0 46 1 1 1 0 4 1 .11662930745082345
    0 0 0 0 21 0 2 1 0 4 1 .11662930745082345
    0 . 1 0 26 1 1 1 0 4 1 .11662930745082345
    0 1 0 0 50 0 0 1 0 4 1 .11662930745082345
    0 1 1 0 25 1 3 0 0 3 1 .11662930745082345
    1 . 0 0 35 0 2 0 0 4 1 .11662930745082345
    0 0 0 0 40 1 2 0 0 4 1 .10115633417167323
    . 0 0 0 48 0 0 1 0 4 1 .11662930745082345
    . . 0 0 47 1 1 0 0 4 1 .11662930745082345
    . 1 0 0 46 0 0 1 0 4 1 .11662930745082345
    . . 0 0 19 1 3 0 0 4 1 .11662930745082345
    . 0 0 0 46 0 0 1 0 4 1 .11662930745082345
    . 0 0 0 42 1 2 1 0 4 1 .11662930745082345
    . 0 0 0 29 0 3 1 0 4 1 .11662930745082345
    0 1 0 0 23 1 3 0 0 4 1 .11662930745082345
    . 0 0 1 52 0 2 1 0 4 1   .098587619521058
    . 0 0 1 44 1 1 0 0 4 1   .098587619521058
    . 0 0 1 37 0 1 1 0 4 1   .098587619521058
    0 1 0 1 35 1 0 0 0 4 1   .098587619521058
    . . 0 1 49 0 0 1 0 4 1   .098587619521058
    . 0 0 1 32 1 2 0 0 4 1   .098587619521058
    . 1 0 1 49 0 0 1 0 4 1   .098587619521058
    . . 0 1 19 1 1 0 0 4 1   .098587619521058
    0 1 0 1 19 0 2 0 0 4 1   .098587619521058
    . 1 0 1 50 1 0 0 0 4 1   .098587619521058
    . 1 0 1 46 0 2 1 0 4 0 .07209680654501288
    . 1 0 1 41 1 2 1 0 3 0 .07209680654501288
    . 0 0 1 46 0 2 1 0 4 1 .07209680654501288
    . 1 0 1 39 1 1 0 0 4 1 .07209680654501288
    . . 1 1 60 0 3 1 0 3 1 .07209680654501288
    . 0 0 1 34 1 0 1 1 3 1 .07209680654501288
    . 1 0 1 57 0 2 1 1 3 1 .05447932486087617
    . 0 0 1 51 1 3 1 0 3 1 .07209680654501288
    . 0 0 1 42 0 1 1 0 3 1 .07209680654501288
    . 0 0 1 46 1 3 1 0 4 1 .06253186969023976
    end
    Really appreciate your help on this matter

    Comment


    • #3
      Patty:
      you will probably receive no reply to your query unless you re-post it on the General forum.
      As an aside, see the FAQ about the aims of the different Stata forums. Thanks.
      Kind regards,
      Carlo
      (Stata 16.0 SE)

      Comment

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